Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866909204672937984 |
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| author | Wang, Fanwen Ferreira, Pedro F. Wu, Yinzhe Munoz, Camila Wen, Ke Luo, Yaqing Huang, Jiahao Pennell, Dudley J. Scott, Andrew D. Nielles-Vallespin, Sonia Yang, Guang |
| author_facet | Wang, Fanwen Ferreira, Pedro F. Wu, Yinzhe Munoz, Camila Wen, Ke Luo, Yaqing Huang, Jiahao Pennell, Dudley J. Scott, Andrew D. Nielles-Vallespin, Sonia Yang, Guang |
| contents | Diffusion tensor cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion and imperfect cardiac triggering. Registration is challenging in DT-CMR due to the low signal-to-noise and various contrasts induced by the diffusion encoding in the myocardium and surrounding organs. Traditional deformable registration corrects through-plane motion but at the risk of destroying the texture information while rigid registration inefficiently discards frames with local deformation. In this study, we explored the possibility of deep learning-based deformable registration on DT-CMR. Based on the noise suppression using low-rank features and diffusion encoding suppression using variational auto encoder-decoder, a B-spline based registration network extracted the displacement fields and maintained the texture features of DT-CMR. In this way, our method improved the efficiency of frame utilization, manual cropping, and computational speed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2309_06598 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration Wang, Fanwen Ferreira, Pedro F. Wu, Yinzhe Munoz, Camila Wen, Ke Luo, Yaqing Huang, Jiahao Pennell, Dudley J. Scott, Andrew D. Nielles-Vallespin, Sonia Yang, Guang Image and Video Processing Diffusion tensor cardiac magnetic resonance (DT-CMR) is a method capable of providing non-invasive measurements of myocardial microstructure. Image registration is essential to correct image shifts due to intra and inter breath-hold motion and imperfect cardiac triggering. Registration is challenging in DT-CMR due to the low signal-to-noise and various contrasts induced by the diffusion encoding in the myocardium and surrounding organs. Traditional deformable registration corrects through-plane motion but at the risk of destroying the texture information while rigid registration inefficiently discards frames with local deformation. In this study, we explored the possibility of deep learning-based deformable registration on DT-CMR. Based on the noise suppression using low-rank features and diffusion encoding suppression using variational auto encoder-decoder, a B-spline based registration network extracted the displacement fields and maintained the texture features of DT-CMR. In this way, our method improved the efficiency of frame utilization, manual cropping, and computational speed. |
| title | Efficient Post-processing of Diffusion Tensor Cardiac Magnetic Imaging Using Texture-conserving Deformable Registration |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2309.06598 |